The good, the bad, and the ugly: impact of analytics and artificial intelligence-enabled personal information collection on privacy and participation in ridesharing

Understanding the Antecedents and Effects of mHealth App Use in Pandemics: A Sequential Mixed-Method Investigation

Pandemics such as COVID-19 pose serious threats to public health and disrupt the established systems for obtaining healthcare services. Mobile health (mHealth) apps serve the general public as a potential method for coping with these exogenous challenges. However, prior research has rarely discussed the antecedents and effects of mHealth apps and their use as a coping method during pandemics. Based on the technology acceptance model, empowerment theory, and event theory, we developed a research model to examine the antecedents (technology characteristics and event strength) and effects (psychological empowerment) of mHealth apps and their use. We tested this research model through a sequential mixed-method investigation. First, a quantitative study based on 402 Chinese mHealth users who used the apps during the COVID-19 pandemic was conducted to validate the theoretical model. A follow-up qualitative study of 191 online articles and reviews on mHealth during the COVID-19 pandemic was conducted to cross-validate the results and explain the unsupported findings of the quantitative study. The results show that (1) the mHealth app characteristics (perceived usefulness and perceived ease of use) positively affect mHealth app use; (2) mHealth app use positively affects the psychological empowerment of mHealth users; and (3) the characteristics of pandemic events (event criticality and event disruption) have positive moderating effects on the relationship between mHealth app characteristics and mHealth app use. This study explains the role of mHealth apps in the COVID-19 pandemic on the micro-level, which has implications for the ways in which mHealth apps are used in response to public pandemics.

An investigation on trust in AI-enabled collaboration: Application of AI-Driven chatbot in accommodation-based sharing economy

Several measures taken to control the spread of the COVID-19 pandemic have severely disrupted the accommodation sharing sector. This study attempts to find solutions to aid the recovery of the accommodation sharing sector via team efforts. Accordingly, we focus on the integration of artificial intelligence (AI) and collaboration. Despite the significant developments in AI technologies, there exists no research considering the application of AI in team collaboration. Utilizing the design science research method and collaboration engineering, we developed an AI-driven prototype system, AI-Driven, for collaboration process recommendation. Qualitative results show that the newly developed tool for collaboration process recommendation has achieved satisfactory performance. Furthermore, we investigated the antecedents and outcomes of trust in the AI-driven collaboration context. From a practical perspective, we propose several solutions to the challenges looming over the accommodation sharing sector according to collaboration deliverables. Furthermore, a system prototype was developed to facilitate collaboration process recommendation and provide procedural guidance.

Urban Infill Development: A Strategy for Saving Peri-Urban Areas in Developing Countries (the Case Study of Ardabil, Iran)

The overall objective of this study was to investigate urban infill development policies as a good solution to counteract urban sprawl and protect the peri-urban area of Ardabil in Northwestern Iran. In this context, we used a mixed methodology (two quantitative methods). Landsat imagery, including a patchy Landsat ETM+ for the year 2000 and a Landsat 8 for the year 2020, was used to map and assess land use to investigate sprawl and land-use change, and ArcGIS was used to investigate the potential for infill development in this city. The results show that between 2000 and 2020, 967 hectares of peri-urban land was lost to urban expansion. CA-Markov projections also showed that 452 hectares will be lost by 2030. The assessment of the city’s internal capacity for infill development showed that more than 999 hectares of land within the city are suitable to support this strategy and provide the land needed for urban expansion over the next decade. Finally, the study of the city’s master plan, which applies to all Iranian cities, discovered that there is a lack of adequate outlook regarding the amount of land available for future urban development, leading to misuse of urban land and urban sprawl in Iranian cities, suggesting that an infill development strategy could be a good way to address this issue.